Category: Hidden Markov models

Adaptive sampling
Adaptive sampling is a technique used in computational molecular biology to efficiently simulate protein folding when coupled with molecular dynamics simulations.
Hierarchical hidden Markov model
The hierarchical hidden Markov model (HHMM) is a statistical model derived from the hidden Markov model (HMM). In an HHMM, each state is considered to be a self-contained probabilistic model. More pre
Hidden semi-Markov model
A hidden semi-Markov model (HSMM) is a statistical model with the same structure as a hidden Markov model except that the unobservable process is semi-Markov rather than Markov. This means that the pr
Hidden Markov model
A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process — call it — with unobservable ("hidden") states. As part of the definition
Brown clustering
Brown clustering is a hard hierarchical agglomerative clustering problem based on distributional information proposed by Peter Brown, William A. Brown, Vincent Della Pietra, , Jennifer Lai, and Robert
Folding@home (FAH or F@h) is a volunteer computing project aimed to help scientists develop new therapeutics for a variety of diseases by the means of simulating protein dynamics. This includes the pr
Layered hidden Markov model
The layered hidden Markov model (LHMM) is a statistical model derived from the hidden Markov model (HMM). A layered hidden Markov model (LHMM) consists of N levels of HMMs, where the HMMs on level i +
Island algorithm
The island algorithm is an algorithm for performing inference on hidden Markov models, or their generalization, dynamic Bayesian networks.It calculates the marginal distribution for each unobserved no